onnx-ir

An in-memory IR that supports the full ONNX spec, designed for graph construction, analysis and transformation.

Getting Started

Via pip:

pip install onnx-ir

Or from source:

pip install git+https://github.com/onnx/ir-py.git

Full ONNX spec support: all valid models representable by ONNX protobuf, and a subset of invalid models (so you can load and fix them).
Low memory footprint: mmap'ed external tensors; unified interface for ONNX TensorProto, Numpy arrays and PyTorch Tensors etc. No tensor size limitation. Zero copies.
Straightforward access patterns: Access value information and traverse the graph topology at ease.
Robust mutation: Create as many iterators as you like on the graph while mutating it.
Speed: Performant graph manipulation, serialization/deserialization to Protobuf.
Pythonic and familiar APIs: Classes define Pythonic apis and still map to ONNX protobuf concepts in an intuitive way.
No protobuf dependency: The IR does not require protobuf once the model is converted to the IR representation, decoupling from the serialization format.

_protocols.py: Interfaces defined for all entities in the IR.
_core.py: Implementation of the core entities in the IR, including Model, Graph, Node, Value, and others.
_enums.py: Definition of the type enums that correspond to the DataType and AttributeType in onnx.proto.
_name_authority.py: The authority for giving names to entities in the graph, used internally.
_linked_list.py: The data structure as the node container in the graph that supports robust iteration and mutation. Internal.
_metadata.py: Metadata store for all entities in the IR.

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